AI Architect - Life Sciences

Roles and Responsibility

• Own the architecture and make it easy to build on.
• Develop and evolve our enterprise reference architecture for generative and agentic AI across models, retrieval, orchestration, reasoning, and governance.
• Build and demonstrate hands-on prototypes, stand up working proofs, and continuously learn as the field evolves.
• Lead solutioning from proof-of-concept to production, including scoping experiments and proving them.
• Set technical standards and own build-vs-buy decisions, running architecture reviews and mentoring engineers.
• Translate technical trade-offs clearly for leadership, clients, and engineering.

Job Requirements

• Production-proven, end-to-end experience in software/AI engineering.
• Hands-on depth across the modern agentic stack, including orchestration frameworks, protocols, RAG and GraphRAG, vector and graph databases, model serving, and LLMOps.
• Practical evaluation and guardrails experience, making model quality measurable and defensible.
• Command of the full span from classical ML/DL to generative to agentic, with the ability to choose the simplest approach that works.
• Cloud and data-platform depth (AWS / Azure / Databricks) and familiarity with semantic layers and lakehouse architectures.
• Excellent communication and stakeholder management skills, including senior leadership and mentoring.
• Experience in regulated industries such as life sciences, pharma, or healthcare, and compliance-aware AI architecture (GxP, GAMP 5, 21 CFR Part 11, ALCOA+ data integrity, EU AI Act / HIPAA awareness).
• Knowledge of graphs, semantic layers, and ontologies as first-class design elements.
• Multi-tenant SaaS or customer-deployed system design experience.
• Public contributions (talks, writing, or open source) on generative/agentic AI architecture.